{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/question-generation/papers/5","list_of":"/task/question-generation","task":"Question Generation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":5,"pages_in_order":7,"rows_per_page":100,"rows":[401,500],"of":664,"counts":{"archive_papers_tagged":664,"with_a_code_link":264,"where_syntology_ran_a_sample":47,"not_listed_spam_title":0,"listed":664,"listed_where_code_ran":47,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":38,"every_run_a_failure_of_syntologys_instrument":9,"listed_with_a_run_with_no_instrument_failure":38,"listed_every_run_a_failure_of_syntologys_instrument":9,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/question-generation","prev":"/task/question-generation/papers/4","next":"/task/question-generation/papers/6","papers":[{"url":null,"slug":"a-weak-supervision-approach-for-predicting","title":"A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"competence-based-question-generation","title":"Competence-based Question Generation","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"does-meta-learning-help-mbert-for-few-shot","title":"Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages?","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"khanq-a-dataset-for-generating-deep-questions","title":"KHANQ: A Dataset for Generating Deep Questions in Education","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lfkqg-a-controlled-generation-framework-with","title":"LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-cqg-a-meta-learning-framework-for-1","title":"Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qsts-a-question-sensitive-text-similarity","title":"QSTS: A Question-Sensitive Text Similarity Measure for Question Generation","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"question-generation-based-on-grammar","title":"Question Generation Based on Grammar Knowledge and Fine-grained Classification","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"type-dependent-prompt-cycleqag-cycle","title":"Type-dependent Prompt CycleQAG : Cycle Consistency for Multi-hop Question Generation","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-generation-of-long-form","title":"Unsupervised Generation of Long-form Technical Questions from Textbook Metadata using Structured Templates","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/calibrating-sequence-likelihood-improves","slug":"calibrating-sequence-likelihood-improves","title":"Calibrating Sequence likelihood Improves Conditional Language Generation","date":"2022-09-30","arxiv_id":"2210.00045","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-choice-question-generation-towards","title":"Multiple-Choice Question Generation: Towards an Automated Assessment Framework","date":"2022-09-23","arxiv_id":"2209.11830","repositories_listed":0,"syntology":null},{"url":null,"slug":"selecting-better-samples-from-pre-trained","title":"Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation","date":"2022-09-22","arxiv_id":"2209.11000","repositories_listed":0,"syntology":null},{"url":"/paper/enhancing-pre-trained-models-with-text","slug":"enhancing-pre-trained-models-with-text","title":"Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation","date":"2022-09-09","arxiv_id":"2209.04179","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-system-for-robot-concept-learning-through","title":"A System For Robot Concept Learning Through Situated Dialogue","date":"2022-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-diversify-for-product-question","title":"Learning to Diversify for Product Question Generation","date":"2022-07-06","arxiv_id":"2207.02534","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-true-false-question-generation-for","title":"Automatic True/False Question Generation for Educational Purpose","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qa-is-the-new-kr-question-answer-pairs-as","title":"QA Is the New KR: Question-Answer Pairs as Knowledge Bases","date":"2022-07-01","arxiv_id":"2207.00630","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-for-question","title":"Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-question-generation-for-personalized","title":"Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems","date":"2022-06-08","arxiv_id":"2206.04187","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-questions-from-wikidata-triples","title":"Generating Questions from Wikidata Triples","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generation-de-question-a-partir-danalyse","title":"Génération de question à partir d’analyse sémantique pour l’adaptation non supervisée de modèles de compréhension de documents (Question generation from semantic analysis for unsupervised adaptation of document understanding models)","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"question-generation-and-answering-for","title":"Question Generation and Answering for exploring Digital Humanities collections","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"un-corpus-annote-pour-la-generation-de","title":"Un corpus annoté pour la génération de questions et l’extraction de réponses pour l’enseignement (An annotated corpus for abstractive question generation and extractive answer for education)","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"v-doc-visual-questions-answers-with-documents","title":"V-Doc : Visual questions answers with Documents","date":"2022-05-27","arxiv_id":"2205.13724","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-question-generation-based-on","title":"Automatic question generation based on sentence structure analysis using machine learning approach","date":"2022-05-25","arxiv_id":"2205.12811","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-self-paced-mixed-distillation-method-for","title":"A Self-Paced Mixed Distillation Method for Non-Autoregressive Generation","date":"2022-05-23","arxiv_id":"2205.11162","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-should-i-ask-a-knowledge-driven-approach","title":"What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys","date":"2022-05-23","arxiv_id":"2205.10977","repositories_listed":0,"syntology":null},{"url":null,"slug":"let-s-talk-striking-up-conversations-via","title":"Let's Talk! Striking Up Conversations via Conversational Visual Question Generation","date":"2022-05-19","arxiv_id":"2205.09327","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-makes-a-question-inquisitive-a-study-on","title":"\"What makes a question inquisitive?\" A Study on Type-Controlled Inquisitive Question Generation","date":"2022-05-17","arxiv_id":"2205.08056","repositories_listed":0,"syntology":null},{"url":null,"slug":"tibert-tibetan-pre-trained-language-model","title":"TiBERT: Tibetan Pre-trained Language Model","date":"2022-05-15","arxiv_id":"2205.07303","repositories_listed":0,"syntology":null},{"url":null,"slug":"fantastic-questions-and-where-to-find-them-2","title":"Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-x-nlg-a-meta-learning-approach-based-on-1","title":"Meta-X_{NLG}: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pseudo-ambiguous-and-clarifying-questions","title":"Pseudo Ambiguous and Clarifying Questions Based on Sentence Structures Toward Clarifying Question Answering System","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-multiple-choice-question-1","title":"Unsupervised multiple-choice question generation for out-of-domain Q&A fine-tuning","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qrelscore-better-evaluating-generated","title":"QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance","date":"2022-04-29","arxiv_id":"2204.13921","repositories_listed":0,"syntology":null},{"url":null,"slug":"question-generation-for-reading-comprehension-1","title":"Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask","date":"2022-04-06","arxiv_id":"2204.02908","repositories_listed":0,"syntology":null},{"url":null,"slug":"vlsp-2021-shared-task-vietnamese-machine","title":"VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension","date":"2022-03-22","arxiv_id":"2203.11400","repositories_listed":0,"syntology":null},{"url":null,"slug":"ask-to-understand-question-generation-for","title":"Ask to Understand: Question Generation for Multi-hop Question Answering","date":"2022-03-17","arxiv_id":"2203.09073","repositories_listed":0,"syntology":null},{"url":null,"slug":"k-vqg-knowledge-aware-visual-question","title":"K-VQG: Knowledge-aware Visual Question Generation for Common-sense Acquisition","date":"2022-03-15","arxiv_id":"2203.07890","repositories_listed":0,"syntology":null},{"url":null,"slug":"indicnlg-suite-multilingual-datasets-for","title":"IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages","date":"2022-03-10","arxiv_id":"2203.05437","repositories_listed":0,"syntology":null},{"url":null,"slug":"question-generation-for-evaluating-cross","title":"Question Generation for Evaluating Cross-Dataset Shifts in Multi-modal Grounding","date":"2022-01-24","arxiv_id":"2201.09639","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-question-generation-with-continual","title":"Unified Question Generation with Continual Lifelong Learning","date":"2022-01-24","arxiv_id":"2201.09696","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-biomedical-information-retrieval","title":"Improving Biomedical Information Retrieval with Neural Retrievers","date":"2022-01-19","arxiv_id":"2201.07745","repositories_listed":0,"syntology":null},{"url":null,"slug":"all-you-may-need-for-vqa-are-image-captions","title":"All You May Need for VQA are Image Captions","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"consecutive-question-generation-with","title":"Consecutive Question Generation with Multitask Joint Reranking and Dynamic Rationale Search","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cooperative-self-training-of-machine-reading","title":"Cooperative Self-training of Machine Reading Comprehension","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-for-biomedical-factoid","title":"Data Augmentation for Biomedical Factoid Question Answering","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"jeff-just-another-efficient-reading","title":"JEFF - Just Another EFFicient Reading Comprehension Test Generation","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-cqg-a-meta-learning-framework-for","title":"Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Graph","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mixqg-neural-question-generation-with-mixed-1","title":"MixQG: Neural Question Generation with Mixed Answer Types","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qafacteval-improved-qa-based-factual-1","title":"QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"true-re-evaluating-factual-consistency","title":"TRUE: Re-evaluating Factual Consistency Evaluation","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"v-doc-visual-questions-answers-with-documents-1","title":"V-Doc: Visual Questions Answers With Documents","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-asp-based-approach-to-answering-natural","title":"An ASP-based Approach to Answering Natural Language Questions for Texts","date":"2021-12-21","arxiv_id":"2112.11241","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-clarification-question","title":"Self-supervised clarification question generation for ambiguous multi-turn conversation","date":"2021-12-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"question-answering-survey-directions","title":"Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices","date":"2021-12-07","arxiv_id":"2112.03572","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-controllability-of-educational","title":"Improving Controllability of Educational Question Generation by Keyword Provision","date":"2021-12-02","arxiv_id":"2112.01012","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-question-generation-from-history","title":"Temporal Question Generation from History Text","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-build-robust-faq-chatbot-with","title":"How to Build Robust FAQ Chatbot with Controllable Question Generator?","date":"2021-11-18","arxiv_id":"2112.03007","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-feasibility-study-of-answer-unaware","title":"A Feasibility Study of Answer-Unaware Question Generation for Education","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-well-composed-text-is-half-done-semantic","title":"A Well-Composed Text is Half Done! Semantic Composition Sampling for Diverse Conditional Generation","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"entity-conditioned-question-generation-for","title":"Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qa-domain-adaptation-using-data-augmentation","title":"QA Domain Adaptation using Data Augmentation and Contrastive Adaptation","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"question-generation-for-reading-comprehension","title":"Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-guided-counterfactual-generation-1","title":"Retrieval-guided Counterfactual Generation for QA","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-multiple-choice-question","title":"Unsupervised multiple-choice question generation for out-of-domain Q\\&A fine-tuning","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"words-of-wisdom-representational-harms-in","title":"Words of Wisdom: Representational Harms in Learning From AI Communication","date":"2021-11-16","arxiv_id":"2111.08581","repositories_listed":0,"syntology":null},{"url":null,"slug":"calculating-question-similarity-is-enough-a","title":"Calculating Question Similarity is Enough: A New Method for KBQA Tasks","date":"2021-11-15","arxiv_id":"2111.07658","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-ai-based-solution-for-enhancing-delivery","title":"An AI-based Solution for Enhancing Delivery of Digital Learning for Future Teachers","date":"2021-11-09","arxiv_id":"2112.01229","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoeqa-auto-encoding-questions-for","title":"AutoEQA: Auto-Encoding Questions for Extractive Question Answering","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"can-question-generation-debias-question-1","title":"Can Question Generation Debias Question Answering Models? A Case Study on Question–Context Lexical Overlap","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"conquest-contextual-question-paraphrasing","title":"ConQuest: Contextual Question Paraphrasing through Answer-Aware Synthetic Question Generation","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"diversity-and-consistency-exploring-visual","title":"Diversity and Consistency: Exploring Visual Question-Answer Pair Generation","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"q-2-evaluating-factual-consistency-in-1","title":"Q^{2}: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"open-domain-clarification-question-generation","title":"Open-domain clarification question generation without question examples","date":"2021-10-19","arxiv_id":"2110.09779","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-abstractive-model-for-generating","title":"A Unified Abstractive Model for Generating Question-Answer Pairs","date":"2021-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cooperative-semi-supervised-transfer-learning","title":"Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension","date":"2021-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-well-do-you-know-your-audience-reader","title":"How Well Do You Know Your Audience? Toward Socially-aware Question Generation","date":"2021-10-16","arxiv_id":"2110.08445","repositories_listed":0,"syntology":null},{"url":null,"slug":"mtg-a-benchmarking-suite-for-multilingual-1","title":"MTG: A Benchmarking Suite for Multilingual Text Generation","date":"2021-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"guiding-visual-question-generation","title":"Guiding Visual Question Generation","date":"2021-10-15","arxiv_id":"2110.08226","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-guided-counterfactual-generation","title":"Retrieval-guided Counterfactual Generation for QA","date":"2021-10-14","arxiv_id":"2110.07596","repositories_listed":0,"syntology":null},{"url":null,"slug":"mmiu-dataset-for-visual-intent-understanding","title":"MMIU: Dataset for Visual Intent Understanding in Multimodal Assistants","date":"2021-10-13","arxiv_id":"2110.06416","repositories_listed":0,"syntology":null},{"url":null,"slug":"simple-or-complex-complexity-controllable","title":"Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model","date":"2021-10-13","arxiv_id":"2110.06560","repositories_listed":0,"syntology":null},{"url":null,"slug":"decision-theoretic-question-generation-for","title":"Decision-Theoretic Question Generation for Situated Reference Resolution: An Empirical Study and Computational Model","date":"2021-10-12","arxiv_id":"2110.06288","repositories_listed":0,"syntology":null},{"url":null,"slug":"i-do-not-understand-what-i-cannot-define","title":"I Do Not Understand What I Cannot Define: Automatic Question Generation With Pedagogically-Driven Content Selection","date":"2021-10-08","arxiv_id":"2110.04123","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-approaches-to-automatic-question","title":"A Survey of Approaches to Automatic Question Generation:from 2019 to Early 2021","date":"2021-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-answers-in-referential-visual","title":"The Impact of Answers in Referential Visual Dialog","date":"2021-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-and-effective-model-for-multi-hop","title":"A Simple and Effective Model for Multi-Hop Question Generation","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"conversational-multi-hop-reasoning-with","title":"Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules","date":"2021-09-17","arxiv_id":"2109.08544","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-unsupervised-question-answering-via","title":"Improving Unsupervised Question Answering via Summarization-Informed Question Generation","date":"2021-09-16","arxiv_id":"2109.07954","repositories_listed":0,"syntology":null},{"url":null,"slug":"question-generation-for-generating-textbook","title":"Question Generation for Generating Textbook Flashcards","date":"2021-09-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reframing-instructional-prompts-to-gptk-s","title":"Reframing Instructional Prompts to GPTk's Language","date":"2021-09-16","arxiv_id":"2109.07830","repositories_listed":0,"syntology":null},{"url":null,"slug":"asking-questions-like-educational-experts","title":"Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data","date":"2021-09-11","arxiv_id":"2109.05179","repositories_listed":0,"syntology":null},{"url":null,"slug":"math-word-problem-generation-with","title":"Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints","date":"2021-09-09","arxiv_id":"2109.04546","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-answer-candidates-for-quizzes-and","title":"Generating Answer Candidates for Quizzes and Answer-Aware Question Generators","date":"2021-08-29","arxiv_id":"2108.12898","repositories_listed":0,"syntology":null},{"url":null,"slug":"invigorate-interactive-visual-grounding-and","title":"INVIGORATE: Interactive Visual Grounding and Grasping in Clutter","date":"2021-08-25","arxiv_id":"2108.11092","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-learning-assistant-in-telugu","title":"Automatic Learning Assistant in Telugu","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-input-representation-granularity","title":"Exploring Input Representation Granularity for Generating Questions Satisfying Question-Answer Congruence","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ji-yu-die-dai-xin-xi-chuan-di-he-hua-dong","title":"基于迭代信息传递和滑动窗口注意力的问题生成模型研究(Question Generation Model Based on Iterative Message Passing and Sliding Windows Hierarchical Attention)","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"250f3857303928ae3c450a0c6da2d500e588d15631944ae7436673c15b41d586","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}